review system design module (#31)

Thanks Sanket and Sumesh for the review
Merging them

courses/systems_design/availability.md

@@ -16,9 +16,9 @@ Availability is generally expressed as “Nines”, common ‘Nines’  are list
 
 ## HA - Availability Serial Components
 
-A System with components is operating in the series If failure of a part leads to the combination becoming inoperable.
+A System with components is operating in the series If the failure of a part leads to the combination becoming inoperable.
 
-For example if LB in our architecture fails, all access to app tiers will fail. LB and app tiers are connected serially.
+For example, if LB in our architecture fails, all access to app tiers will fail. LB and app tiers are connected serially.
 
 
 The combined availability of the system is the product of individual components availability
@@ -30,11 +30,11 @@ The combined availability of the system is the product of individual components
 
 ## HA - Availability Parallel Components
 
-A System with components is operating in parallel If failure of a part leads to the other part taking over the operations of the failed part.
+A System with components is operating in parallel If the failure of a part leads to the other part taking over the operations of the failed part.
 
-If we have more than one LB and if rest of the LBs can take over the traffic during one LB failure then LBs are operating in parallel
+If we have more than one LB and if the rest of the LBs can take over the traffic during one LB failure then LBs are operating in parallel
 
-The combined availability of the system is 
+The combined availability of the system is
 
 *A = 1 - ( (1-Ax) x (1-Ax) x ….. )*
 
@@ -74,11 +74,12 @@ The combined availability of the system is
 
 **WHY:** Maximize availability and ensure data handling semantics are preserved.  
 
-**KEY TAKEAWAYS:** Strive for active/active rather than active/passive solutions, they have a lesser risk of cross over being unreliable. Use LB and right load balancing methods to ensure reliable failover. Model and build your data systems to ensure data is correctly handled when crossover happens. Generally DB systems follow active/passive semantics for writes. Masters accept writes and when master goes down, follower is promoted to master(active from being passive) to accept writes. We have to be careful here that the cutover never introduces more than one masters. This problem is called a split brain.
+**KEY TAKEAWAYS:** Strive for active/active rather than active/passive solutions, they have a lesser risk of cross over being unreliable. Use LB and the right load balancing methods to ensure reliable failover. Model and build your data systems to ensure data is correctly handled when crossover happens. Generally, DB systems follow active/passive semantics for writes. Masters accept writes and when the master goes down, the follower is promoted to master(active from being passive) to accept writes. We have to be careful here that the cutover never introduces more than one master. This problem is called a split brain.
 
 ## Applications in SRE role
-1. SRE works on deciding an acceptable SLA and make sure system is available to achieve the SLA
-2. SRE is involved in architecture design right from building the data center to make sure site is not affected by network switch, hardware, power or software failures
+
+1. SRE works on deciding an acceptable SLA and make sure the system is available to achieve the SLA
+2. SRE is involved in architecture design right from building the data center to make sure the site is not affected by a network switch, hardware, power, or software failures
 3. SRE also run mock drills of failures to see how the system behaves in uncharted territory and comes up with a plan to improve availability if there are misses. 
 https://engineering.linkedin.com/blog/2017/11/resilience-engineering-at-linkedin-with-project-waterbear
 

courses/systems_design/conclusion.md

@@ -1,3 +1,3 @@
 # Conclusion
 
-Armed with these principles, we hope the course will give a fresh perspective to design software systems. It might be over engineering to get all this on day zero. But some are really important from day 0 like eliminating single points of failure, making scalable services by just increasing replicas. As a bottleneck is reached, we can split code by services, shard data to scale. As the organisation matures, bringing in [chaos engineering](https://en.wikipedia.org/wiki/Chaos_engineering) to measure how systems react to failure will help in designing robust software systems.
+Armed with these principles, we hope the course will give a fresh perspective to design software systems. It might be over-engineering to get all this on day zero. But some are really important from day 0 like eliminating single points of failure, making scalable services by just increasing replicas. As a bottleneck is reached, we can split code by services, shard data to scale. As the organization matures, bringing in [chaos engineering](https://en.wikipedia.org/wiki/Chaos_engineering) to measure how systems react to failure will help in designing robust software systems.

courses/systems_design/fault-tolerance.md

@@ -32,28 +32,28 @@ Common failure metrics that get measured and tracked for any system.
 ### Fault Tolerance - Fault Isolation Terms
 Systems should have a short circuit. Say in our content sharing system, if “Notifications” is not working, the site should gracefully handle that failure by removing the functionality instead of taking the whole site down. 
 
-Swimlane is one of the commonly used fault isolation methodology. Swimlane adds a barrier to the service from other services so that failure on either of them won’t affect the other. Say we roll out a new feature ‘Advertisement’ in our content sharing app.
+Swimlane is one of the commonly used fault isolation methodologies. Swimlane adds a barrier to the service from other services so that failure on either of them won’t affect the other. Say we roll out a new feature ‘Advertisement’ in our content sharing app.
 We can have two architectures
 ![Swimlane](images/swimlane-1.jpg)
 
-If Ads are generated on the fly synchronously during each Newsfeed request, the faults in Ads feature gets propagated to Newsfeed feature. Instead if we swimlane “Generation of Ads” service and use a shared storage to populate Newsfeed App, Ads failures won’t cascade to Newsfeed and worst case if Ads don’t meet SLA , we can have Newsfeed without Ads.
+If Ads are generated on the fly synchronously during each Newsfeed request, the faults in the Ads feature get propagated to the Newsfeed feature. Instead if we swimlane the “Generation of Ads” service and use a shared storage to populate Newsfeed App, Ads failures won’t cascade to Newsfeed, and worst case if Ads don’t meet SLA , we can have Newsfeed without Ads.
 
-Let's take another example, we come up with a new model for our Content sharing App. Here we roll out enterprise content sharing App where enterprises pay for the service and the content should never be shared outside the enterprise. 
+Let's take another example, we have come up with a new model for our Content sharing App. Here we roll out an enterprise content sharing App where enterprises pay for the service and the content should never be shared outside the enterprise. 
 
 ![Swimlane-principles](images/swimlane-2.jpg)
 
 ### Swimlane Principles
 
-**Principle 1:** Nothing is shared (also known as “share as little as possible”). The less that is shared within a swim lane, the more fault isolative the swim lane becomes. (as shown in Enterprise usecase)
+**Principle 1:** Nothing is shared (also known as “share as little as possible”). The less that is shared within a swim lane, the more fault isolative the swim lane becomes. (as shown in Enterprise use-case)
 
 **Principle 2:** Nothing crosses a swim lane boundary. Synchronous (defined by expecting a request—not the transfer protocol) communication never crosses a swim lane boundary; if it does, the boundary is drawn incorrectly. (as shown in Ads feature)
 
 ### Swimlane Approaches
 **Approach 1:** Swim lane the money-maker. Never allow your cash register to be compromised by other systems. (Tier 1  vs Tier 2 in enterprise use case)
 
-**Approach 2:** Swim lane the biggest sources of incidents. Identify the recurring causes of pain and isolate them.(if Ads feature is in code yellow, swim laning it is the best option)
+**Approach 2:** Swim lane the biggest sources of incidents. Identify the recurring causes of pain and isolate them. (if Ads feature is in code yellow, swim laning it is the best option)
 
-**Approach 3:** Swim lane natural barriers. Customer boundaries make good swim lanes.(Public vs Enterprise customers)
+**Approach 3:** Swim lane natural barriers. Customer boundaries make good swim lanes. (Public vs Enterprise customers)
 
 
 #### Refer
@@ -64,5 +64,3 @@ Let's take another example, we come up with a new model for our Content sharing
 1. Work with the DC tech or cloud team to distribute infrastructure such that its immune to switch or power failures by creating fault zones within a Data Center
 https://docs.microsoft.com/en-us/azure/virtual-machines/manage-availability#use-availability-zones-to-protect-from-datacenter-level-failures
 2. Work with the partners and design interaction between services such that one service breakdown is not amplified in a cascading fashion to all upstreams
-
-

courses/systems_design/intro.md

@@ -17,7 +17,7 @@ Thinking about and designing for scalability, availability, and reliability of l
 
 Individual software components’ scalability and reliability concerns like e.g. Databases, while the same scalability principles and thinking can be applied, these individual components have their own specific nuances when scaling them and thinking about their reliability.
 
-More light will be shed on concepts rather than on setting up and configuring components like Loadbalancers to achieve scalability, availability and reliability of systems
+More light will be shed on concepts rather than on setting up and configuring components like Loadbalancers to achieve scalability, availability, and reliability of systems
 
 ## Course Contents
 
@@ -34,7 +34,7 @@ So, how do you go about learning to design a system?
 *” Like most great questions, it showed a level of naivety that was breathtaking. The only short answer I could give was, essentially, that you learned how to design a system by designing systems and finding out what works and what doesn’t work.”
 Jim Waldo, Sun Microsystems, On System Design*
 
-	
+    
 As software and hardware systems have multiple moving parts, we need to think about how those parts will grow, their failure modes, their inter-dependencies, how it will impact the users and the business.
 
 There is no one-shot method or way to learn or do system design, we only learn to design systems by designing and iterating on them.

courses/systems_design/scalability.md

@@ -11,19 +11,19 @@ An always-on service is said to be scalable if adding resources to facilitate re
 
 ## Scalability - AKF Scale Cube
 
-The [Scale Cube](https://akfpartners.com/growth-blog/scale-cube) is a model for segmenting services, defining microservices, and scaling products. It also creates a common language for teams to discuss scale related options in designing solutions. Following section talks about certain scaling patterns based on our inferences from AKF cube
+The [Scale Cube](https://akfpartners.com/growth-blog/scale-cube) is a model for segmenting services, defining microservices, and scaling products. It also creates a common language for teams to discuss scale related options in designing solutions. The following section talks about certain scaling patterns based on our inferences from the AKF cube
 
 ## Scalability - Horizontal scaling
 
-Horizontal scaling stands for  cloning of an application or service such that work can easily be distributed across instances with absolutely no bias.
+Horizontal scaling stands for cloning of an application or service such that work can easily be distributed across instances with absolutely no bias.
 
-Lets see how our monolithic application improves with this principle
+Let's see how our monolithic application improves with this principle
 
 ![Horizontal Scaling](images/horizontal-scaling.jpg)
 
-Here DB is scaled separately from the application. This is to let you know each component’s scaling capabilities can be different. Usually web applications can be scaled by adding resources unless there is no state stored inside the application. But DBs can be scaled only for Reads by adding more followers but Writes have to go to only one master to make sure data is consistent. There are some DBs which support multi master writes but we are keeping them out of scope at this point. 
+Here DB is scaled separately from the application. This is to let you know each component’s scaling capabilities can be different. Usually, web applications can be scaled by adding resources unless there is no state stored inside the application. But DBs can be scaled only for Reads by adding more followers but Writes have to go to only one master to make sure data is consistent. There are some DBs that support multi-master writes but we are keeping them out of scope at this point. 
 
-Apps should be able to differentiate between Read and Writes to choose appropriate DB servers. Load balancers can split traffic between identical servers  transparently.
+Apps should be able to differentiate between Reads and Writes to choose appropriate DB servers. Load balancers can split traffic between identical servers transparently.
 
 **WHAT:**  Duplication of services or databases to spread transaction load.
 
@@ -31,23 +31,23 @@ Apps should be able to differentiate between Read and Writes to choose appropria
 
 **HOW TO USE:** Simply clone services and implement a load balancer. For databases, ensure that the accessing code understands the difference between a read and a write.
 
-**WHY:** Allows for fast scale of transactions at the cost of duplicated data and functionality.
+**WHY:** Allows for the fast scale of transactions at the cost of duplicated data and functionality.
 
-**KEY TAKEAWAYS:** This is fast to implement, is low cost from a developer effort perspective, and can scale transaction volumes nicely. However, they tend to be high cost from the perspective of the operational cost of data. Cost here means if we have 3 followers and 1 Master DB, the same database will be stored as 4 copies in the 4 servers. Hence added storage cost
+**KEY TAKEAWAYS:** This is fast to implement, is a low cost from a developer effort perspective, and can scale transaction volumes nicely. However, they tend to be high cost from the perspective of the operational cost of data. The cost here means if we have 3 followers and 1 Master DB, the same database will be stored as 4 copies in the 4 servers. Hence added storage cost
 
-### Refer	
+### Refer   
 - [https://learning.oreilly.com/library/view/the-art-of/9780134031408/ch23.html](https://learning.oreilly.com/library/view/the-art-of/9780134031408/ch23.html) 
 
 ### Scalability Pattern - Load Balancing
 
-Improves the distribution of workloads across multiple computing resources, such as computers, a computer cluster, network links, central processing units, or disk drives. Commonly used technique is load balancing traffic across identical server clusters. Similar philosophy is used to load balance traffic across network links by [ECMP](https://en.wikipedia.org/wiki/Equal-cost_multi-path_routing), disk drives by [RAID](https://en.wikipedia.org/wiki/RAID) etc
+Improves the distribution of workloads across multiple computing resources, such as computers, a computer cluster, network links, central processing units, or disk drives. A commonly used technique is load balancing traffic across identical server clusters. A similar philosophy is used to load balance traffic across network links by [ECMP](https://en.wikipedia.org/wiki/Equal-cost_multi-path_routing), disk drives by [RAID](https://en.wikipedia.org/wiki/RAID),etc
 
 Aims to optimize resource use, maximize throughput, minimize response time, and avoid overload of any single resource.
 Using multiple components with load balancing instead of a single component may increase reliability and availability through redundancy. In our updated architecture diagram we have 4 servers to handle app traffic instead of a single server
 
 The device or system that performs load balancing is called a load balancer, abbreviated as LB.
 
-#### Refer	
+#### Refer  
 - [https://en.wikipedia.org/wiki/Load_balancing_(computing)](https://en.wikipedia.org/wiki/Load_balancing_(computing))
 - [https://blog.envoyproxy.io/introduction-to-modern-network-load-balancing-and-proxying-a57f6ff80236](https://blog.envoyproxy.io/introduction-to-modern-network-load-balancing-and-proxying-a57f6ff80236)
 - [https://learning.oreilly.com/library/view/load-balancing-in/9781492038009/](https://learning.oreilly.com/library/view/load-balancing-in/9781492038009/)
@@ -60,7 +60,7 @@ What does an LB do?
 
 
 #### Service discovery: 
-What backends are available in the system? In our architecture, 4 servers are available to serve App traffic. LB acts as a single endpoint that clients can use  transparently to reach one of the 4 servers.
+What backends are available in the system? In our architecture, 4 servers are available to serve App traffic. LB acts as a single endpoint that clients can use transparently to reach one of the 4 servers.
 
 #### Health checking: 
 What backends are currently healthy and available to accept requests? If one out of the 4 App servers turns bad, LB should automatically short circuit the path so that clients don’t sense any application downtime
@@ -72,10 +72,10 @@ What algorithm should be used to balance individual requests across the healthy
 Common Load Balancing Methods
 
 #### Least Connection Method 
-directs traffic to the server with the fewest active connections. Most useful when there are a large number of persistent connections in the traffic unevenly distributed between the servers. Works if clients maintain long lived connections
+directs traffic to the server with the fewest active connections. Most useful when there are a large number of persistent connections in the traffic unevenly distributed between the servers. Works if clients maintain long-lived connections
 
 #### Least Response Time Method 
-directs traffic to the server with the fewest active connections and the lowest average response time. Here response time is used to provide feedback of server’s health
+directs traffic to the server with the fewest active connections and the lowest average response time. Here response time is used to provide feedback of the server’s health
 
 #### Round Robin Method 
 rotates servers by directing traffic to the first available server and then moves that server to the bottom of the queue. Most useful when servers are of equal specification and there are not many persistent connections.
@@ -105,7 +105,7 @@ CDNs are added closer to the client’s location. If the app has static data lik
 Eg
 media-exp1.licdn.com is a domain used by Linkedin to serve static content
 
-Here a CNAME points the domain to the DNS of CDN provider
+Here a CNAME points the domain to the DNS of the CDN provider
 
 dig media-exp1.licdn.com +short
 
@@ -133,28 +133,28 @@ Microservices can scale transactions, data sizes, and codebase sizes. They are m
 
 **WHY:** Allows for efficient scaling of not only transactions but also very large data sets associated with those transactions. It also allows for the efficient scaling of teams.
 
-**KEY TAKEAWAYS:** Microservices allow for efficient scaling of transactions, large data sets, and can help with fault isolation.  It  helps reduce the communication overhead of teams. The codebase becomes less complex as disjoint features are decoupled and spun as new services  thereby letting each service scale independently specific to its requirement.
+**KEY TAKEAWAYS:** Microservices allow for efficient scaling of transactions, large data sets, and can help with fault isolation.  It helps reduce the communication overhead of teams. The codebase becomes less complex as disjoint features are decoupled and spun as new services thereby letting each service scale independently specific to its requirement.
 
-### Refer	
+### Refer   
 - https://learning.oreilly.com/library/view/the-art-of/9780134031408/ch23.html 
 
 ## Scalability - Sharding
 
-This pattern represents the separation of work based on attributes that are looked up or determined at the time of the transaction. Most often, these are implemented as splits by requestor, customer, or client.
+This pattern represents the separation of work based on attributes that are looked up to or determined at the time of the transaction. Most often, these are implemented as splits by requestor, customer, or client.
 
 Very often, a lookup service or deterministic algorithm will need to be written for these types of splits.
 
-Sharding aids in scaling transaction growth, scaling instruction sets, and decreasing processing time (the last by limiting the data necessary to perform any transaction). This  is  more effective at scaling growth in customers or clients. It can aid with disaster recovery efforts, and limit the impact of incidents to only a specific segment of customers.
+Sharding aids in scaling transaction growth, scaling instruction sets, and decreasing processing time (the last by limiting the data necessary to perform any transaction). This is more effective at scaling growth in customers or clients. It can aid with disaster recovery efforts, and limit the impact of incidents to only a specific segment of customers.
 
 ![Sharding-block-1](images/sharding-1.jpg)
 
-Here the auth  data is sharded based on user names so that DBs can respond faster as the amount of data DBs have to work on has drastically reduced during queries.
+Here the auth data is sharded based on user names so that DBs can respond faster as the amount of data DBs have to work on has drastically reduced during queries.
 
 There can be other ways to split 
 
 ![Sharding-block-2](images/sharding-2.jpg)
 
-Here the whole data centre is split and replicated and clients are directed to a data centre based on their geography. This helps in improving performance as clients are directed to the closest Data centre and performance increases as we add more data centres. There are some replication and consistency overhead with this approach one needs to be aware of.  This also gives fault tolerance by rolling out test features to one site and rollback if there is an impact to that geography
+Here the whole data center is split and replicated and clients are directed to a data center based on their geography. This helps in improving performance as clients are directed to the closest data center and performance increases as we add more data centers. There are some replication and consistency overhead with this approach one needs to be aware of.  This also gives fault tolerance by rolling out test features to one site and rollback if there is an impact to that geography
 
 **WHAT:** This is very often a split by some unique aspect of the customer such as customer ID, name, geography, and so on.
 
@@ -172,11 +172,12 @@ Here the whole data centre is split and replicated and clients are directed to a
 
 
 ## Applications in SRE role
-1. SREs in coordination with the network team work on how to map users traffic to a particular site. 
+
+1. SREs in coordination with the network team work on how to map users' traffic to a particular site.
 https://engineering.linkedin.com/blog/2017/05/trafficshift--load-testing-at-scale
 2. SREs work closely with the Dev team to split monoliths to multiple microservices that are easy to run and manage
-3. SREs work on improving Load Balancers' reliability, service discovery and performance
+3. SREs work on improving Load Balancers' reliability, service discovery, and performance
 4. SREs work closely to split Data into shards and manage data integrity and consistency. 
 https://engineering.linkedin.com/espresso/introducing-espresso-linkedins-hot-new-distributed-document-store
-5. SREs work to set up, configure and improve CDN cache hit rate.
+5. SREs work to set up, configure, and improve the CDN cache hit rate.